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import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys

import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import cartopy.crs as ccrs
import matplotlib.pyplot as plt

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive
from ind_setup.plotting import plot_base_map, plot_map_subplots, plot_bar_probs
from ind_setup.core import fontsize

sys.path.append("../../../functions")
from data_downloaders import download_ERDDAP_data

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 15
     12 import matplotlib.pyplot as plt
     14 sys.path.append("../../../../indicators_setup")
---> 15 from ind_setup.plotting_int import plot_timeseries_interactive
     16 from ind_setup.plotting import plot_base_map, plot_map_subplots, plot_bar_probs
     17 from ind_setup.core import fontsize

ModuleNotFoundError: No module named 'ind_setup'

#Area of interest
lon_range  = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]

shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)

update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"

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base_url = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/md50_exp.csv'
dataset_id = 'MD50'

if update_data:
    date_ini = '1998-01-01T00:00:00Z'
    date_end = '2023-12-01T00:00:00Z'
    data = download_ERDDAP_data(base_url, dataset_id, date_ini, date_end, lon_range, lat_range)
    data_xr = data.set_index(['latitude', 'longitude', 'time']).to_xarray()
    data_xr['time'] = pd.to_datetime(data_xr.time)
    data_xr = data_xr.coarsen(longitude=2, latitude=2, boundary = 'pad').mean()
    data_xr.to_netcdf(op.join(path_data, f'griddap_{dataset_id}.nc'))
else:
    data_xr = xr.open_dataset(op.join(path_data, f'griddap_{dataset_id}.nc'))

ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, data_xr.mean(dim='time')[dataset_id], transform=ccrs.PlateCarree(), 
                cmap = 'YlGnBu', vmin = 0.8, vmax = 1.3)
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label='Phytoplankton (µm)')
plt.savefig(op.join(path_figs, 'F16_phytoplankton_mean_map.png'), dpi=300, bbox_inches='tight')

data_y = data_xr.resample(time='1YE').mean()
plot_map_subplots(data_y, 'MD50', shp_eez = shp_eez, cmap = 'YlGnBu', vmin = 0.4, vmax = 1.6, cbar = 1)

data_an = data_y - data_xr.mean(dim='time')
plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.3, vmax=.3, cbar = 1)

dict_plot = [{'data' : data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : 'Median Phytoplankton Size - MEAN AREA'},]

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12));
fig.write_html(op.join(path_figs, 'F16_phytoplankton_mean_trend.html'), include_plotlyjs="cdn")

loc = [7.35, 134.48]
dict_plot = [{'data' : data_xr.sel(longitude=loc[1], latitude=loc[0], method='nearest').to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : f'Median Phytoplankton Size at [{loc[0]}, {loc[1]}]'},]

ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
ax.plot(loc[1], loc[0], '*', markersize = 12, color = 'royalblue', transform=ccrs.PlateCarree(), label = 'Location Analysis')
ax.legend()

<matplotlib.legend.Legend at 0x18504ce00>

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12));